Digital government and service delivery: An examination of performance and prospects
Bibliographic record
Abstract
Abstract Since the emergence of electronic or digital government two decades ago, the delivery of public services online has been a centrepiece in efforts to leverage the Internet and improve the performance of the public sector. Prodded by comparisons to banks and online retailers, governments at all levels have been enticed by the dramatically lower costs of a transaction online versus one involving mail, a telephone call centre, or in‐person service facility. Yet such comparators have also masked a much more complicated story for public sector service innovation and delivery reform. The recent advent of mobility further complicates this landscape since the term can be interpreted in one of two (partially related) manners: first, as a newer online channel via mobile devices that accentuates the search for efficiency as integration; and second, as a basis for more participative public engagement in the governance of service design and delivery. Drawing upon three inter‐related typologies of public sector governance (traditional public administration, new public management, and public value management), this article examines the evolution of a partially digitized sector service architecture, its mixed performance to date, and the challenges ahead. Specific attention is devoted to the Liberal Government's initial sign posts as well as the increasingly pressing inter‐governmental dimensions to more digitized service delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".